Convex Programming in Data Science II

Optimisation problems involving polynomials arise in a wide variety of contexts, including operational research, statistics, probability, finance, computer science, structural engineering, statistical physics, combinatorial chemistry, computational biology and algorithmic graph theory. They are however extremely challenging to solve, both in theory and practice. Existing algorithms and software are capable of solving only very small instances to proven optimality, unless they have some amenable structure, such as sparsity or convexity.

Read more at: http://www.newton.ac.uk/programmes/POP/

Optimisation problems involving polynomials arise in a wide variety of contexts, including operational research, statistics, probability, finance, computer science, structural engineering, statistical physics, combinatorial chemistry, computational biology and algorithmic graph theory. They are however extremely challenging to solve, both in theory and practice. Existing algorithms and software are capable of solving only very small instances to proven optimality, unless they have some amenable structure, such as sparsity or convexity. Read more at: http://www.newton.ac.uk/programmes/POP/

Share Convex Programming in Data Science II

Sign up to save your podcasts

Convex Programming in Data Science II

Convex Programming in Data Science II

More shows like Polynomial Optimisation

Philosophy

Public Lectures from the Faculty of Law, University of Cambridge

CRASSH

Faculty of Classics

LCIL International Law Centre Podcast

Medieval History Seminars

Darwin – Darwin College Lecture Series 2009

Cambridge Creative Writing Centre - Crime and Thriller

The Mathematics of Liquid Crystals

The Stanton Lectures 2012-13